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Damage Detection of Rail Fastening System Through Deep Learning and Vehicle-Track Coupled Dynamics

  • Zhandong YuanEmail author
  • Shengyang Zhu
  • Wanming Zhai
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Owing to the rapid development of the rail transportation, the health monitoring of the track structure becomes a challenging problem. This article presents a novel approach to carry out damage detection and localization of fastening systems along the rail based on deep learning and vehicle-track coupled dynamics analysis. A convolutional neural network (CNN) is designed to learn optimal damage-sensitive features from the rail acceleration response automatically and identify the damage location of fastening systems, leading to a high detecting accuracy. The vehicle-track coupled dynamics model incorporating different damage level of fastening systems is adopted to generate labeled dataset to train the proposed network. The advantage of this approach is that CNN learns to extract the optimal damage-sensitive features from the raw dynamical response data automatically without the need of computing and selecting hand-crafted features manually. T-SNE is applied to manifest the super feature extraction capability of CNN. Thereafter, the trained network is estimated on the testing dataset to validate its generation capability. The results reveal a good performance of the proposed method.

Keywords

Vibration Damage detection Convolutional neural network Vehicle-track coupled dynamics 

References

  1. 1.
    Jamshidi, A., Faghih-Roohi, S., Hajizadeh, S., et al.: A big data analysis approach for rail failure risk assessment. Risk Anal. 37, 1495–1507 (2017)CrossRefGoogle Scholar
  2. 2.
    Singh, M., Singh, S., Jaiswal, J., et al.: Autonomous rail track inspection using vision based system. In: IEEE International Conference on Computational Intelligence for Homeland Security & Personal Safety, pp. 56–59 (2007)Google Scholar
  3. 3.
    Cha, Y.J., Choi, W.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Aided Civ. Infrastruct. Eng. 32(5), 361–378 (2017)CrossRefGoogle Scholar
  4. 4.
    Lee, J.J., Lee, J.W., Yi, J.H., et al.: Neural networks-based damage detection for bridges considering errors in baseline finite element models. J. Sound Vib. 280(3–5), 555–578 (2005)CrossRefGoogle Scholar
  5. 5.
    Sun, Z., Chang, C.C.: Structural damage assessment based on wavelet packet transform. J. Struct. Eng. 128(10), 1354–1361 (2002)CrossRefGoogle Scholar
  6. 6.
    Lin, Y.Z., Nie, Z.H., Ma, H.W.: Structural damage detection with automatic feature-extraction through deep learning. Comput. Aided Civ. Infrastruct. Eng. 32(12), 1025–1046 (2017)CrossRefGoogle Scholar
  7. 7.
    Abdeljaber, O., Avci, O., Kiranyaz, S., et al.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhai, W.M., Wang, K.Y., Cai, C.B.: Fundamentals of vehicle-track coupled dynamics. Veh. Syst. Dyn. 47(11), 1349–1376 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhai, W.M.: Two simple fast integration methods for large-scale dynamic problems in engineering. Int. J. Numer. Meth. Eng. 39(24), 4199–4214 (1996)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, Lille (2015)Google Scholar
  11. 11.
    TensorFlow. www.tensorflow.org
  12. 12.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations, San Diego (2015)Google Scholar
  13. 13.
    Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Train and Track Research Institute, State Key Laboratory of Traction PowerSouthwest Jiaotong UniversityChengduChina

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